/*
 GRT MIT License
 Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software 
 and associated documentation files (the "Software"), to deal in the Software without restriction, 
 including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, 
 and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, 
 subject to the following conditions:
 
 The above copyright notice and this permission notice shall be included in all copies or substantial 
 portions of the Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT 
 LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, 
 WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE 
 SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 */

/*
 GRT KNN Example
 This examples demonstrates how to initialize, train, and use the KNN algorithm for classification. 
 
 The K-Nearest Neighbor (KNN) Classifier is a simple classifier that works well on basic recognition problems, however it can be slow for real-time prediction if there are a large number of training examples and is not robust to noisy data. 
 
 In this example we create an instance of a KNN algorithm and then train the algorithm using some pre-recorded training data.
 The trained KNN algorithm is then used to predict the class label of some test data.
 
 This example shows you how to:
 - Create an initialize the KNN algorithm and set the number of neighbors to use for clasification
 - Load some LabelledClassificationData from a file and partition the training data into a training dataset and a test dataset
 - Train the KNN algorithm using the training dataset
 - Test the KNN algorithm using the test dataset
 - Manually compute the accuracy of the classifier
*/

#include "GRT.h"
using namespace GRT;

int main (int argc, const char * argv[])
{

    //Create a new KNN classifier with a K value of 10
    KNN knn(10);
    knn.setNullRejectionCoeff( 10 );
    knn.enableScaling( true );
    knn.enableNullRejection( true );
    
    //Train the classifier with some training data
    ClassificationData trainingData;
    
    if( !trainingData.load("KNNTrainingData.grt") ){
        cout << "Failed to load training data!\n";
        return EXIT_FAILURE;
    }
    
    //Use 20% of the training dataset to create a test dataset
    ClassificationData testData = trainingData.partition( 80 );
    
    //Train the classifier
    if( !knn.train( trainingData ) ){
        cout << "Failed to train classifier!\n";
        return EXIT_FAILURE;
    }
    
    //Save the knn model to a file
    if( !knn.save("KNNModel.grt") ){
        cout << "Failed to save the classifier model!\n";
        return EXIT_FAILURE;
    }
    
    //Load the knn model from a file
    if( !knn.load("KNNModel.grt") ){
        cout << "Failed to load the classifier model!\n";
        return EXIT_FAILURE;
    }
    
    //Use the test dataset to test the KNN model
    double accuracy = 0;
    for(UINT i=0; i<testData.getNumSamples(); i++){
        //Get the i'th test sample
        UINT classLabel = testData[i].getClassLabel();
        vector< double > inputVector = testData[i].getSample();
        
        //Perform a prediction using the classifier
        bool predictSuccess = knn.predict( inputVector );
        
        if( !predictSuccess ){
            cout << "Failed to perform prediction for test sampel: " << i <<"\n";
            return EXIT_FAILURE;
        }
        
        //Get the predicted class label
        UINT predictedClassLabel = knn.getPredictedClassLabel();
        vector< double > classLikelihoods = knn.getClassLikelihoods();
        vector< double > classDistances = knn.getClassDistances();
        
        //Update the accuracy
        if( classLabel == predictedClassLabel ) accuracy++;
        
        cout << "TestSample: " << i <<  " ClassLabel: " << classLabel << " PredictedClassLabel: " << predictedClassLabel << endl;
    }
    
    cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
    
    return EXIT_SUCCESS;

}
